Upload 2 files

app.py

@@ -0,0 +1,42 @@
+
+import streamlit as st
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFaceHub
+
+@st.cache_resource
+def load_vector_store():
+    loader = TextLoader("data/sample.txt")
+    documents = loader.load()
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    chunks = splitter.split_documents(documents)
+
+    embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    db = FAISS.from_documents(chunks, embedding_model)
+    return db
+
+def main():
+    st.title("📄 Ask Your Document (RAG with LangChain + Hugging Face)")
+    st.write("Upload a document, ask questions, and get answers powered by open-source LLMs!")
+
+    query = st.text_input("Enter your question:")
+    if query:
+        db = load_vector_store()
+        qa_chain = RetrievalQA.from_chain_type(
+            llm=HuggingFaceHub(
+                repo_id="google/flan-t5-base", 
+                model_kwargs={"temperature": 0.5, "max_length": 256}
+            ),
+            retriever=db.as_retriever(),
+            return_source_documents=True
+        )
+        result = qa_chain.run(query)
+        st.write("### 📌 Answer")
+        st.write(result)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+
+streamlit
+langchain
+openai
+faiss-cpu
+sentence-transformers